Managing sales opportunities within an organization

ABSTRACT

Disclosed is a system and method for a consistent scoring system that illustrates the likelihood of successfully closing a sales deal or sales agreement. This allows sales professionals to allocate more time on strategy where it&#39;s needed most. It guides sales professionals on eliminating vulnerabilities or weaknesses in closing a sales deal and leveraging strengths to improve the likelihood to successfully close more sales deals. The present invention is computationally efficient in solving a combinatorial optimization algorithm. Being computational efficient enables the present invention to be scalable in order to handle more data and transactions. The results of the present invention provide insights in which a sales organization can act upon by re-using knowledge of previous interactions with a buyer or potential buyer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation in part ofU.S. patent application Ser. No. 15/381,790, filed on Dec. 16, 2016,entitled “Computer-Implemented System And Methods For Providing SalesInformation To Sales Professionals” which claims the benefit of U.S.Provisional Application No. 62/310,686 filed on Mar. 19, 2016, entitled“Systems And Methods For Assisting Sales Professionals in OptimizingTheir Sales Results”, the teaching of each of these patent applicationsis hereby incorporated by reference in their entirety.

FIELD OF THE DISCLOSURE

The present invention generally relates to the field of salesinformation providing sales information to sale professionals and morespecifically managing sales between organizations.

BACKGROUND

Business-to-business (B2B) Sales Professionals are facing unprecedentedpressure to perform—they are expected to sell more—and faster. But to besuccessful in today's environment, they must overcome many obstacles. Afirst obstacle needed to be overcome by B2B Sales Professionals is thatthey are underserved by technology. Tools like Customer RelationshipManagement (CRM) systems can result in adding an administrative burdenmore than they help the sales team. They are often instituted for thebenefit of the company, not necessarily for that of the salesprofessional using them.

Another obstacle needed to be overcome by B2B Sales Professionals isthat they lack the skills and resources to navigate an increasinglycomplex buyer landscape. With access to a wealth of information fromsearch engines and social media, today's buyers no longer rely onsalespeople for the educational component of their analysis. This meansthat salespeople have far less time to nurture leads through the salesprocess.

A further obstacle needed to be overcome by B2B Sales Professionals isthat research now shows that, on average, at least 5 people are requiredto formally sign off on a B2B purchase. The authority to make a purchasenow rests within a larger group, most of who are at different stages ofthe buying journey.

Still another obstacle needed to be overcome by B2B Sales Professionalsis that the classroom sales training they typically receive isineffective, expensive, and outdated. Traditional sales educationtechniques that were a mainstay for decades now fall short in preparingB2B sales professionals to better align themselves with today's savvybuyers.

The confluence of these factors and obstacles faced by B2B SalesProfessionals has created a longer and more complex sales cycle. Tosolve these problems, there are a number of patents directed at salesmethodologies, sales training, and sales optimization. While there havebeen a number of technologies and systems for facilitating sales, nonehave provided a simple methodology for overcoming the above mentionedobstacles.

Therefore, a long-standing need exists for simple, yet modern, salesmethodologies to help Sales Professionals, such as B2B SalesProfessionals, optimize their chances of winning more deals. A furtherneed exists for novel computer-implemented systems and methods that areconfigured to provide sales information to sales professionals. Finally,a need exists for novel computer-implemented systems and methods thatare configured to provide sales information, such as sales methodology,sales training, and sales optimization, to one or more salesprofessionals.

SUMMARY OF THE INVENTION

The present invention provides a novel system and method for aconsistent scoring system that illustrates the likelihood ofsuccessfully closing a sales deal or sales agreement. This allows salesprofessionals to allocate more time on strategy where it's needed most.It guides sales professionals on eliminating vulnerabilities orweaknesses in closing a sales deal and leveraging strengths to improvethe likelihood to successfully close more sales deals.

The present invention is computationally efficient in solving acombinatorial optimization algorithm. Being computational efficientenables the present invention to be scalable in order to handle moredata and transactions. The results of the present invention provideinsights in which a sales organization can act upon by re-usingknowledge of previous interactions with a buyer or potential buyer.

In one example the present invention provides and system and method formanaging sales within organizations. The method begins with receivinginput from a user through a client device, the input identifies a set ofsource key members. Next, a database of a set of target key members isaccessed. Stored in this database is a set of source key members and theset of target key members that include a data record with values for aplurality of additional sales attributes, such as RIPAA (Role, Impactscore, Priority score, Advocacy score, and Access score) values, in asales decision process. In one example, the process further includes afirst additional sales attribute representing a number of source keyplayers to use. Next, for each source key member in the set of sourcekey members and each of target key member in the set of target keymembers, performing:

-   -   1. normalizing a similarity measurement by calculating a        distance between corresponding additional sales attributes in        the plurality of additional sales attributes for each of the        source key members with each of the target key members to form        an array of similarity measurement costs; and    -   2. applying a combinatorial optimization algorithm for solving        an assignment problem using the array of similarity measurement        costs, such that one corresponding additional sales attribute of        each target key member is assigned to one corresponding        additional sales attribute of each source key member, whereby a        total similarity measurement cost of assignment is minimized.

Next, a sub-set of target key members in the set of target key memberswith a lowest total similarity measurement cost of assignment isidentified. The process ends by presenting the sub-set of target keymembers that has been identified with the lowest total similaritymeasurement cost of assignment to the user.

In one example, the process further includes further comprisesnormalizing the values for the plurality of additional sale attributesto within a numerical range for each of the source key members.

In another example, filters for and combination of each additional salesattribute,

-   -   a sales status of a sales transaction, e.g., open sales        transaction, a successful transaction and an unsuccessful        transaction,    -   sales competitor,    -   sales stage e.g., pre-qualified, qualified, pricing, and        proposal, and/or    -   other text string        are included as a user selection before presenting the sub-set        of results.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, and which together with the detailed description below areincorporated in and form part of the specification, serve to furtherillustrate various embodiments and to explain various principles andadvantages all in accordance with the present disclosure, in which:

FIG. 1 illustrates a high-level client-server environment, according toone aspect of the present invention;

FIG. 2 illustrates examples of a novel interface for providing input ofa Role, an Impact score, a Priority score, an Advocacy score, and anAccess score (RIPAA), according to one aspect of the present invention;

FIG. 3 illustrates evolution of sales process from managing structuredsales conversations using RIPAA to sales coaching to uncover areas ofimprovement with sales professionals;

FIG. 4 is a graphical illustration of matching source deals and targetdeals of four (4) key players as step 1, according to one aspect of thepresent invention;

FIG. 5 is another a graphical illustration of matching source deals andtarget deals as step 1 with five (5) key players in comparison to four(4) key players in FIG. 4, according to another aspect of the presentinvention;

FIG. 6 is an example of a matrix of the source key players and targetkey players illustrating RIPAA values of the deals of FIG. 4 as step 2,according to one aspect of the present invention;

FIG. 7 is an example of a matrix of the source and target with a firstorder normalization of FIG. 6 as step 3, according to one aspect of thepresent invention;

FIG. 8 is an example of a matrix of the source and target with a secondorder normalization of FIG. 7 as step 4 illustrating a similaritymeasurement costs, according to one aspect of the present invention;

FIG. 9 is an example of a matrix of the source and target using acombinatorial optimization algorithm for solving an assignment problemof the similarity measurement costs of FIG. 8 as step 5, according toone aspect of the present invention;

FIG. 10 is an example of results of lowest total similarity measurementcosts of assignment found in FIG. 9 as step 6, according to one aspectof the present invention;

FIG. 11 is an example of using a combinatorial optimization algorithmfor solving an assignment problem of the similarity measurement costs ofFIG. 8, according to one aspect of the present invention;

FIG. 12 is an example of using a combinatorial optimization algorithmfor solving an assignment problem of the similarity measurement costs ofFIG. 11, according to one aspect of the present invention;

FIG. 13 illustrates filtering of matching source deals and target deals,according to an example; and

FIG. 14A and FIG. 14B is a novel graphical display of matching sourcedeals and target deals of using aspects of FIG. 4 thru FIG. 13,according to an example.

FIG. 15 is an overall process flow of managing sales with anorganization, according to an example;

FIG. 16 is an overall process flow of finding matching deal attributesof FIG. 15, according to an example; and

FIG. 17 is an overall process flow of generating reports of FIG. 15,according to an example.

DETAILED DESCRIPTION

As required, detailed embodiments are disclosed herein; however, it isto be understood that the disclosed embodiments are merely examples andthat the systems and methods described below are embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the disclosed subject matter in virtually anyappropriately detailed structure and function. Further, the terms andphrases used herein are not intended to be limiting, but rather, toprovide an understandable description.

Non-Limiting Definitions

Generally, the terms “a” or “an”, as used herein, are defined as one ormore than one. The term plurality, as used herein, is defined as two ormore than two. The term another, as used herein, is defined as at leasta second or more. The terms “including” and “having,” as used herein,are defined as comprising (i.e., open language). The term “coupled,” asused herein, is defined as “connected,” although not necessarilydirectly, and not necessarily mechanically. The term “configured to”describes hardware, software or a combination of hardware and softwarethat is adapted to, set up, arranged, built, composed, constructed,designed or that has any combination of these characteristics to carryout a given function. The term “adapted to” describes hardware, softwareor a combination of hardware and software that is capable of, able toaccommodate, to make, or that is suitable to carry out a given function.The phrase “at least one of A and B” means either A or B separately orboth A and B.

The terms “application”, “software”, “software code” or “computersoftware” refers to any set of instructions operable to cause a computerto perform an operation. Software code may be operated on by a “rulesengine” or processor. Thus, the methods and systems of the presentinvention may be performed by a computer or computing device having aprocessor based on instructions received by computer applications andsoftware.

The phrase “calculating a distance” means a distance of two points on atwo dimensional or x-y place. For example, the distance in twodimensions the distance (d) between (x₁, y₂) and (x₂, y₂) is given by:d=√{square root over ((x₂−x₁)²+(y₂−y₁)²)}. The present invention extendsthis concept for a distance between two groups or vectors of RIPAAvalues. The distance between two vectors of RIPAA values is given byd=√{square root over((x_(rs)−x_(rt))²+(x_(is)−x_(it))²+(x_(ps)−x_(pt))²+(x_(ads)−x_(adt))²+(x_(acs)−x_(act))²)}in which the values are Xrs=Role Source, Xrt=Role Target, Xis=ImpactSource, Xit=Impact Target, Xps=Priority Source, Xpt=Priority Target,Xads=Advocacy Source, Xadt=Advocacy Target, Xacs=Accessibility Source,and Xact=Accessibility target.

The term “client device” as used herein is a type of computer orcomputing device comprising circuitry and configured to generallyperform functions such as recording audio, photos, and videos;displaying or reproducing audio, photos, and videos; storing,retrieving, or manipulation of electronic data; providing electricalcommunications and network connectivity; or any other similar function.Non-limiting examples of electronic devices include: personal computers(PCs), workstations, laptops, tablet PCs including the iPad, cell phonesincluding iOS phones made by Apple iOS phones, Android OS phones,digital music players, or any electronic device capable of runningcomputer software and displaying information to a user, memory cards,other memory storage devices, digital cameras, external battery packs,external charging devices, and the like.

The term “combinatorial optimization algorithm” is used to find anoptimal object from a finite set of objects. In many such problems,exhaustive search is not tractable. It operates on the domain of thoseoptimization problems in which the set of feasible solutions is discreteor can be reduced to discrete, and in which the goal is to find the bestsolution. Examples of combinatorial optimization algorithm includeHungarian algorithm, Kuhn-Munkres algorithm, simplex algorithm, andothers.

The term “computer” refers to a machine, apparatus, or device that iscapable of accepting and performing logic operations from software code.

The term “computer readable medium” as used herein refers to any mediumthat participates in providing instructions to the processor forexecution. A computer readable medium may take many forms, including butnot limited to, non-volatile media, and volatile media.

The term “confirmation” is portion of the workflow for maintenance andrepair in which a party, typically a requestor or dispatcher mustaffirmatively respond to a prompt for the workflow to continue down amaintenance path or repair path. In the event of no confirmation isreceived, the workflow will branch to an alternative path that puts theorder in a holding or cancelled state.

The term “data network” or “network” shall mean an infrastructurecapable of connecting two or more computers such as client deviceseither using wires or wirelessly allowing them to transmit and receivedata. Non-limiting examples of data networks may include the internet orwireless networks or (i.e. a “wireless network”) which may include Wi-Fiand cellular networks, Bluetooth, and near field communications. Forexample, a network may include a local area network (LAN), a wide areanetwork (WAN) (e.g., the Internet), a mobile relay network, ametropolitan area network (MAN), an ad hoc network, a telephone network(e.g., a Public Switched Telephone Network (PSTN)), a cellular network,or a voice-over-IP (VoIP) network.

The term “database” shall generally mean a digital collection of data orinformation. The present invention uses novel methods and processes tostore, link, and modify information such digital images and videos anduser profile information. For the purposes of the present disclosure, adatabase may be stored on a remote server and accessed by a clientdevice through the internet (i.e., the database is in the cloud) oralternatively in some embodiments the database may be stored on theclient device or remote computer itself (i.e., local storage).

The term “DEAL” shall generally refer to an agreement or compromisebetween a buyer, the entity seeking to make a purchase, and seller oruser, the entity seeking to make a sale, to transact goods and/orservices at an agreed upon price.

The term “key player” shall generally refer to any person representingthe buyer entity of the DEAL and who are determined by the sales personas having a degree of influence over the success of completing the DEAL.

The phrase “normalizing values” means taking input, for example RIPAAinput, in one given range or unit of measure and scaling or convertingit to a common range or numerical range. The common range is typicallybetween 0 and 1 or between 0 and 10. The normalized data makes it easierto compare data and other statistical operations.

The term “PRESCOT” is a coined acronym for Predictive Sales Closing Toolto refer to aspects of the present invention that are being marketed bythe patent owner DealCoachPro Inc.

The term “sales person” or “sales professional” generally refers to anyperson representing the seller entity of the DEAL and is the user ofthis system for the purpose of receiving aid and assistance towards thecompletion of one or more DEALS.

The term “sales attribute” includes one or more of Role, Impact,Priority, Advocacy and Access with the coined acronym (RIPAA). RIPAA isdefined in the above-identified previous patent applications which havebeen incorporated by reference in the first paragraph. For convenience asummary of these terms are defined here: 1) a role of at least one keymember in the set of the plurality of key members in the customer'sorganization in a sales decision process; 2) an impact scorerepresenting an influence of the key member in completing a salestransaction; 3) a priority score representing a level of lessor orgreater importance being allocated by the key member to the salestransaction; 4) an advocacy score representing an amount of support bythe key member for completion of the sale transaction; and 5) an accessscore representing an amount of direct access with the key member.

The term “similarity measurement” is a measure on how close two salesattributes, such as the RIPAA values, are between a source key play andtarget key player. It is referred to as a “costs” i.e. “similaritymeasurement costs” to be consistent with language used in the literaturefor a combinatorial optimization algorithm. One measurement ofsimilarity is by “calculating a distance” as described above. Howeverother measurements of similarity between two values, especially thoseused in the field of statistics, can also be used.

The term “total similarity measurement costs” is a measure of assigningsimilarity costs such that one corresponding additional sales attributeof each target key member is assigned to one corresponding additionalsales attribute of each source key member and vice-versa, that is, onecorresponding additional sales attribute of each source key member isassigned to one corresponding additional sales attribute of each targetkey member. This assignment of costs is performed by permuting the rowsand columns of a matrix of similarity measurement costs.

Overview

The present invention is a tool for salespeople to provide instantinsights into key sales. These insights help each sales person or salesprofessional on the team provide valuable support without taking timeaway from the sales team to get real-time updates. The tool provides theability to salespeople to manage and analyze customer interactions anddata throughout the customer lifecycle, with the goal of improvingcustomer relationships and assisting in driving sales.

The present invention provides a tool to help sales professional,optimize their chances of successfully completing more sale deals orDEALs. Sales information is provided to sales professionals. The salesinformation includes sales methodology, sales training, and salesoptimization, to sales professionals. More specifically, the presentinvention improves the outcome of the “source” opportunity a salesrep isworking on through the use of historic data which closest match thesource opportunities.

The present invention provides actionable tips to guide salesprofessionals on eliminating vulnerabilities or weaknesses in closing aDEAL and leveraging strengths to improve the likelihood to successfullyclose more DEALs.

The present invention provides a consistent scoring system thatillustrates the likelihood of successfully closing a DEAL. This allowssales professionals to allocate more time on strategy where it's neededmost.

The present invention is computationally efficient in solving acombinatorial optimization algorithm. Being computational efficientenables the present invention to be scalable in order to handle moredata and transactions. The results of the present invention provideinsights in which a sales organization can act upon by re-usingknowledge of previous interactions with a buyer or potential buyer.

Client Server Environment

Turning now to FIG. 1 illustrates a high-level client-serverenvironment, according to one aspect of the present invention. Shown aresome of the physical components which may comprise a system forproviding sales information to sales professionals (“the system”) 100according to some embodiments is presented. The system 100 is configuredto facilitate the transfer of data and information between one or moreaccess points 170, 172, client devices 120, 122, 124, 126, and servers160 over a data network 150. Each client device 120, 122, 124, 126 maysend data to and receive data from the data network 150 through anetwork connection 140, 142, 144, 146 with an access points 170, 172. Adata store 162 accessible by the server 160 may contain one or moredatabases with database records such as sales attribute values includingRIPAA for target key players as described further below. The data maycomprise any information pertinent to one or more users 130, 132, 134,136 input into the system 100 including information on or describing oneor more users 130, 132, 134, 136, information on or describing one ormore seller entities, information on or describing one or more buyerentities, information on or describing one or more DEALS, informationrequested by one or more users 130, 132, 134, 136, information suppliedby one or more users 130, 132, 134, 136, and any other information whicha user 130, 132, 134, 136 may be provided such as for training andeducational purposes.

In this example, the system 100 comprises at least one client device120, 122, 124, 126 (but preferably more than two client devices 120,122, 124, 126) configured to be operated by one or more users 130, 132,134, 136. Client devices 120, 122, 124, 126 can be mobile devices, suchas laptops, tablet computers, personal digital assistants, smart phones,and the like, that are equipped with a wireless network interfacecapable of sending data to one or more servers 160 with access to one ormore data stores 162 over a network 150 such as a wireless local areanetwork (WLAN) 172. Additionally, client devices 120, 122, 124, 126 canbe fixed devices, such as desktops, workstations, and the like, that areequipped with a wireless or wired network interface capable of sendingdata to one or more servers 160 with access to one or more data stores(no shown) over a wireless 170 or wired local area network 150. Thepresent invention may be implemented on at least one client device 120,122, 124, 126 and/or server 160 programmed to perform one or more of thesteps described herein. In some embodiments, more than one client device120, 122, 124, 126 and/or server 160 may be used, with each beingprogrammed to carry out one or more steps of a method or processdescribed herein.

In some embodiments, the system 100 may be configured to facilitate thecommunication of information to and from one or more users 130, 132,134, 136, through their respective client devices 120, 122, 124, 126,and servers 160 of the system 100. Users 130, 132, 134, 136 of thesystem 100 may include one or more sales professionals and any otherindividual associated with a seller entity. Typically, users 130, 132,134, 136 describe individuals that desire to create or facilitate theformation of a DEAL with a buyer entity in order to transact goodsand/or services at an agreed upon price. The user 130, 132, 134, 136 mayprovide data and information describing a DEAL and data and informationdescribing one or more key players to the system 100 and the system 100may provide sales information to the user 130, 132, 134, 136 inreal-time which may be used to create or facilitate the formation of theDEAL and which may otherwise not be available to the user 130, 132, 134,136.

RIPAA Slider Assist

FIG. 2 illustrates examples of a novel interface 200 for providing inputof a sales attribute of a customer according to one aspect of thepresent invention. The sales attributes can include a Role (not shown),an Impact score 260, a Priority score 262, an Advocacy score 264, and anAccess score 266 (RIPAA). In this example a user is not sure how toassign a slider value to a given sales attribute (262, 264, 266). Morespecifically, the user is not sure how to assign an Impact score 260using a slider. The user selects slider assist 218 to be ON. Once theslider assist is set to ON, a series of qualifying questions 210 thru250 are presented related to the specific sales attribute. Depending onthe answers from the user e.g., Yes 212, No 214, and Not Sure 216, thesale attribute, shown as a slider, is automatically set.

RIPAA to PRESCOT

FIG. 3 illustrates evolution of sales process from managing structuredsales conversations using RIPAA to accelerate sales by using data acrossorganization by uncovering areas of improvements with salesprofessionals, according to one aspect of the present invention. Thepresent invention builds on the sales attributes previous disclosed(RIPAA) 304 to provide PRECSOT (Predictive Sales Closing Tool) 312. Asfurther described below, PRESCOT provides insights to other similarDEALs through using computational efficiency, namely a combination ofEuclidean geometry and linear optimization. Insights are gained fromother DEALS.

PRESCOT—Step 1 of Selecting or Filter a Number of Key Players

FIG. 4 is a graphical illustration of matching source and target dealsas step 1, according to one aspect of the present invention. In thisexample the source deal is been selected or filter to use four (4) keyplayers in a DEAL. Only those other previous DEALS that also have four(4) key players for each target deal (Target Deal1, Target Deal2, TargetDeal3, Target Deal 4) is selected as a target. The target deals, TargetDeal1, Target Deal2, Target Deal3, Target Deal 4, as part of theinformation in a database with RIPAA and other information previouslystored for a sales organization with sales professionals. Note that thenumber of key players can, and often change, overtime for a givenDEAL—Target Deal1, Target Deal2, Target Deal3, Target Deal 4. Forexample Target Deal1 shows 2 key players on a first date, four keyplayers on a second date, five key players on a third date, and eightkey players on a fourth date. Also note that each Target Deal1, TargetDeal2, and Target Deal3 can have multiple instances based on time i.e.snap shots. Where Target Deal4 is shown with only one snap shot. In oneexample the system defaults to using the most recent match of five keyplayers in Target Deal3 as shown. However, other filters such dealswithin a certain time period can be identified.

Turning now FIG. 5 shown is another a graphical illustration of matchingsource and target deals as step 1 with five (5) key players incomparison to four (4) key players in FIG. 4. Note that the informationfor the given deals DEAL—Target Deal1, Target Deal2, Target Deal3,Target Deal 4 are identical as that of FIG. 4. However, because a filteror choice was made to use five (5) key players to compare a source dealto the target deals, the target deals selected are different than thetarget deals selected in FIG. 4.

PRESCOT—Step 2 of Placing RIPPA Values in Matrix

FIG. 6 is an example of a matrix 602 of the source key players andtarget key player of FIG. 4 as step 2, according to one aspect of thepresent invention. In this matrix representation 602, the RIPAA valuesand other values of the Source Team Key Players (S-KP) are shown acrossthe top of the matrix as S-KP 1, S-KP 2, S-KP 3, S-KP 4. The top row ofeach cell 610 thru 646 is the RIPAA values for each Source Team KeyPlayer 650 thru 656. Going down the matrix are the Target Key Players(T-KP) as T-KP 1, T-KP 2, T-KP 3, T-KP 4. The bottom row of each cell610 thru 646 is the RIPAA values for each Target Team Key Player 660thru 666.

PRESCOT—Step 3 of First Order Normalization

FIG. 7 is an example of a matrix 702 of the source and target with afirst order normalization of FIG. 6 as step 3, according to one aspectof the present invention. Note all the values from FIG. 6 (top row andbottom row) for each cell 710 thru 746 are normalized within a givenrange. In this example the range is 1 to 10. A decision maker isassigned a value of 10, whereas a decision influencer is assigned avalue of 5 however it is important to note that other values and rangescould be used within the true scope and spirit of the present invention.

PRESCOT—Step 4 of Second Order Normalization

FIG. 8 is an example of a matrix 802 of the source and target with asecond order normalization of FIG. 7 as step 4, according to one aspectof the present invention. The second order normalization in this exampleuses a version of the distance formula. Here the each RIPAA set ofvalues (Role, Impact, Priority, Advocacy and Access with the coinedacronym) or vector a Source Team Key Player is compared against eachRIPAA set of value or vector of each Target Team Key Player d=√{squareroot over((x_(rs)−x_(rt))²+(x_(is)−x_(it))²+(x_(ps)−x_(pt))²+(x_(ads)−x_(adt))²+(x_(acs)−x_(act))²)}.850. The top and bottom values of each cell 710 thru 746 shown in FIG. 7is placed in the formula 850 above. The results of each cell 810 thru846 shown in FIG. 8. The values 852 are Xrs=Role Source, Xrt=RoleTarget, Xis=Impact Source, Xit=Impact Target, Xps=Priority Source,Xpt=Priority Target, Xads=Advocacy Source, Xadt=Advocacy Target,Xacs=Accessibility Source, and Xact=Accessibility target. This resultsin a RIPAA proximity score or distance score for each cell as shown.

PRESCOT—Step 5 of Combinatorial Optimization Algorithm Normalization

FIG. 9 is an example of a matrix 902 of the source and target using acombinatorial optimization algorithm for solving an assignment problemof the similarity measurement costs of FIG. 8 as step 5, according toone aspect of the present invention. In this example, the HungarianAlgorithm is run across all cells cell 810 thru 846 in the matrix 802 ofFIG. 8. The results of the Hungarian Algorithm are placed in each cell910 thru 946 as shown. Examples of how the Hungarian Algorithm works ina matrix formulation is found here at online URLs(https://en.wikipedia.org/wiki/Hungarian_algorithm) and(https://www.youtube.com/watch?v=rrfFTdO2Z7I) and(https://www.youtube.com/watch?v=ezSx8OyBZVc), the teachings of which,are hereby incorporate by reference in their entirety. This is furtherdescribed below with reference to FIG. 11 and FIG. 12.

In the matrix formulation, we are given a nonnegative n×n matrix, wherethe element in the i-th row and the j-th column represents the RIPAAvalues of the j-th Source Team Key Player compared, by the distanceformula above, to the RIPAA values of the i-th Target Team Key Player.The Hungarian Algorithm finds a combination of source to target tosource RIPAA values, such that RIPAA vector for each Source Team KeyPlayer compared with RIPAA value of each Target Key Player is minimum.

Stated differently, note that combinatorial optimization algorithm is tofind the lowest comparison value using each column of the matrix. It isthe overall value not the minimum value per column or per row that isfound. The values that are circled in the cell on the matrix on showcombination of values found from a finite set of objects. Here is shownthat the minimum combination of values (circled in each column) acrossthe matrix 902 is given by 1.41+7.55+4.24+2.24=15.44. It is important tonote that the Hungarian does not find the smallest value per row or percolumn but rather the lowest value of values across the entire matrixfor each row. This will be further described below in the sectionentitled Combinatorial Optimization Algorithm.

Proximity of DEALS Analyzed

FIG. 10 is an example of results of lowest sub-set of proximity dealsanalyzed in FIG. 9 as step 6, according to one aspect of the presentinvention. Shown is a ranking 1002 of five of the results target keyplayers of the Hungarian Algorithm with the lowest scores (i.e. thehighest proximity of the RIPAA values of source key players comparedwith the RIPPA values of target key players. Note the lower the score,the more closely match the deal. Shown are Company R, Company J2,Company K, University MM, Company DZ.

Combinatorial Optimization Algorithm Example

FIG. 11 is an example of using a combinatorial optimization algorithmfor solving an assignment problem of FIG. 8, according to one aspect ofthe present invention. For simplicity, the values in cell for table 1102match the values in cells 810 thru 846 in table 802 of FIG. 8. Values.In step 1, the minimum is identified for each row. These minimum valuesare shown in the right most column of table 1102. This minimum value foreach row is subtracted for each cell value in that row resulting intable 1104 as shown. Next, the minimum value for each column isidentified. The minimum value for each column is shown in the bottom rowof table 1104. The minimum value for each column is subtracted for eachcell value in that column resulting in table 1106 as shown in step 2.Next in step 3, as shown in table 1108, the minimum number of verticaland horizontal lines are identified to cover each zero in the matrix ashown in table 1108. The total number of lines required to cover eachzero is compared against the dimension of the matrix. In the event thenumber of lines is less than the dimension of the matrix, the optimalsolution is not yet identified. Otherwise if the number of lines isequal to the dimension of the matrix, the optimal solution isidentified. In this example there are four (4) lines needed. Thedimension m of the matrix is 4. Since m=four (4) lines, the optimalsolutions is found. If Step 4 is required, the process follows thesesub-steps of:

-   -   a. Find the lowest value from the list of values that are        uncovered (no lines running through them).    -   b. Subtract the lowest value from the uncovered values.    -   c. Add the lowest value to the values where the lines intersect    -   d. Replay Step 3, whereby the goal is to find a scenario where        the minimum number of vertical/horizontal lines match the matrix        value m=4 in this scenario. Once achieved, the process continues        to step 5.

Step 5 is shown in FIG. 12 matrix 1202. The process begins with the rowor column that has the minimum number of zeros. The number of zeros foreach column is shown in the bottom row and the number of zeros for eachcolumn is shown in the right most column. Note that it is possible for arow or column to have more than one zero. In this example there are afew rows or columns with only one zero. In one example the processbegins randomly from the row or column with the minimum numbers ofzeros. It does not matter where the process starts the assignment aslong as the row or column has the minimum number of zeros. Note theallocated cell match the cells identified in matrix 902 in FIG. 9 above.

Generating Reports

FIG. 13 illustrates filtering of matching source and target deals ofFIG. 13, according to an example based on one more characteristics. Thecharacteristics status of the deal state 1310. The deal state 1310 shownare open 1312, won 1314, and lost 1316. Another characteristic isCompetitors 1320. Competitors are companies 1322 through 1330. Thecharacteristics of stages of deals 1340. In one example the Sales Stagesare setup by the company. The sales stages can be thought of as acompany's funnel. Different companies typically setup these stagesdifferently. The Deal state is either open, won or lost. Thecharacteristic of stages of deals are identifying 1342, validating 1344,building consensus 1346, closing 1348, closed won 1350, and closed lost1352.

FIG. 14 is a novel graphical display of matching source and target dealsof using aspects of FIG. 4 thru FIG. 13, according to an example. Notethat some of the selection for filters from FIG. 13 are shown at topportions 1410, 1420. The selection of filters shown are deal owner 1412,competitors 1414, close date 1422, sales stage 1424, value 1426. Insection 1430. Shown are two graphs deal legitimacy 1440 and dealposition 1450. Shown are each of the target key players 1442 thru 1448with their RIPAA values graphed. For convenience 1442, 1452 matches1462, 1444, 1454 matches 1464, 1446, 1456 matches 1466 and 1448, 1458matches 1468.

Also shown in area in 1470 in strengths 1472 and vulnerabilities 1474listed from notes for source key player.

Overall Process Flow

FIG. 15 is an overall process flow 1500 of managing sales with anorganization, according to an example. The process begins in step 1502and immediately proceeds to step 1504. In step 1504 a list of previousdeals (i.e., target deals) is retrieved from more databases withdatabase records such as sales attribute values including RIPAA fortarget key players. This list or set of target key players is based onthe number of key players to match in the source deal. Two examples of anumber of key players, one for four (4) key members is shown in FIG. 4and one for five (5) key members is shown in FIG. 5. The processproceeds to step 1506.

In step 1506 the RIPPA values for the source deal with the given numberof key plays is compared to each target deal identified in step 1504each with the same number of given number of key players. This isdescribed in Step 1 through Step 6 above with reference to FIG. 5 thruFIG. 10 above. More details are described in FIG. 16 below. The processcontinues to step 1508.

In step 1508, a test is made to see if all the targets deals identifiedin step 1504 are processed. In response to more target deals identifiedto be process, then the process continues back to step 1506 to beprocessed. Otherwise, in response to no more target deals to beprocessed, then the flow continues to step 1510.

In step 1510, target deals found that have the lowest of thecombinatorial optimization algorithm as compared with a settablethreshold are identified. The settable threshold allows a use to definehow close of comparison of RIPAA values of source to the target areidentified. In one example, just the lowest scores of the combinatorialoptimization algorithm are identified for the source compared with thetarget deals. The identified targets are used to create a report in step1512 (described further in FIG. 16 below) and the process ends in step1514. Otherwise, if source deals compared with target deals within agiven threshold are not found, the process ends in step 1514.

FIG. 16 is an overall process flow 1600 of finding matching dealattributes of step 1506 in FIG. 15, according to an example. The processstarts in step 1600 and immediately proceeds to step 1602.

In step 1602 the number of key players in source and target aresearched. As described above with reference to FIG. 4 and FIG. 5, theremay be one or more snap shots in time that have a number of key playersthat match the source key members. Stated differently each target dealmay, over time, have more than one instance or revision of key playersthat match to source key members. The process continues to step 1604.

In step 1604 a test is made is made to see if any target deals haverevisions that match the number key players in the source deal. Inresponse to a target deal if no deal revisions is found the processcontinues to step 1610 to end. Otherwise, there are revisions in targetdeals in which the number of key players match the source deal theprocess continues to step 1606.

In step 1606 all the revisions in the target deal with the same numberof key players as the source key player are identified and the processflows to step 1608.

In step 1608, the RIPPA values for the source deal with the given numberof key plays is compared to each target deal revision identified in step1606 each with the same number of given number of key players. This isdescribed in Step 1 through Step 6 above with reference to FIG. 5 thruFIG. 10 above. The process continues to step 1610 to end.

FIG. 17 is an overall process flow 1700 of generating reports of FIG.17, according to an example. The process starts in step 1702 andproceeds to step 1704. In step 1704 the top n matching deals isidentified. For example for n=5 the top five target deals as shown inFIG. 10. The number n is settable by the user.

Non-Limiting Examples

Although specific embodiments of the subject matter have been disclosed,those having ordinary skill in the art will understand that changes aremade to the specific embodiments without departing from the spirit andscope of the disclosed subject matter. The scope of the disclosure isnot to be restricted, therefore, to the specific embodiments, and it isintended that the appended claims cover any and all such applications,modifications, and embodiments within the scope of the presentdisclosure.

What is claimed is:
 1. A computer implemented method for managing saleswithin organizations, the method comprising: receiving input from a userthrough a client device, the input identifies a set of source keymembers; accessing a database of a set of target key members, whereineach of the set of source key members and the set of target key membersinclude a data record with values for a plurality of additional salesattributes in a sales decision process, wherein each of the set ofsource key members and the set of target key members include a datarecord with values for the plurality of additional sales attributes inthe sales decision process further includes one or more of a role in theset of target key members in a sales decision process; an impact scorerepresenting an influence in the set of target key members in completinga sales transaction; a priority score representing a level of lessor orgreater importance being allocated by the set of target key members tothe sales transaction; an advocacy score representing an amount ofsupport by the set of target key members for completion of the saletransaction; and an access score representing an amount of direct accesswith the set of target key members; for each source key member in theset of source key members and each of target key member in the set oftarget key members, performing normalizing a similarity measurement bycalculating a distance between corresponding additional sales attributesin the plurality of additional sales attributes for each of the sourcekey members with each of the target key members to form an array ofsimilarity measurement costs; and applying a combinatorial optimizationalgorithm for solving an assignment problem using the array ofsimilarity measurement costs, such that one corresponding additionalsales attribute of each target key member is assigned to onecorresponding additional sales attribute of each source key member,whereby a total similarity measurement cost of assignment is minimized;identifying a sub-set of target key members in the set of target keymembers with a lowest total similarity measurement cost of assignment;and automatically presenting the sub-set of target key members in one ormore of a deal legitimacy graph with one axis representing the impactscore and another axis representing the priority score, wherein a deallegitimacy position on the deal legitimacy graph is indicated in one ofa plurality of different areas based on the lowest total similaritymeasurement cost of assignment, and a deal position graph with one axisrepresenting the advocacy score and another axis representing the accessscore, wherein a deal position on the deal position graph is indicatedin one of a plurality of different areas based on the lowest totalsimilarity measurement cost of assignment.
 2. The computer implementedmethod of claim 1, further comprising for each source key member in theset of source key members, normalizing the values for the plurality ofadditional sale attributes to within a numerical range.
 3. The computerimplemented method of claim 1, wherein the receiving input from the userthrough the client device, the input identifies the set of source keymembers further includes the set of source key members with a firstsales attribute; and wherein the accessing a database of the set oftarget key members includes accessing the set of target key members thatmatch the first sales attribute.
 4. The computer implemented method ofclaim 1, further comprising: receiving input from the user through theclient device, the input identifies at least one additional salesattribute to filter; and wherein the presenting the sub-set of targetkey members that has been identified with the lowest total cost to theuser further includes filtering before presenting sub-set of target keymembers that match the at least one additional sales attribute.
 5. Thecomputer implemented method of claim 1, further comprising: receivinginput from the user through the client device, the input identifies atleast one additional status of a sales transaction; and wherein thepresenting the sub-set of target key members that has been identifiedwith the lowest total cost to the user further includes filtering beforepresenting sub-set of target key members that match the at least oneadditional status of the sales transaction.
 6. The computer implementedmethod of claim 5, wherein the at least one additional status of thesales transaction is at least one of open sales transaction, asuccessful transaction and an unsuccessful transaction.
 7. The computerimplemented method of claim 1, further comprising: receiving input fromthe user through the client device, the input identifies at least onecompetitor; and wherein the presenting the sub-set of target key membersthat has been identified with the lowest total cost to the user furtherincludes filtering before presenting sub-set of target key members thatmatch the at least one competitor.
 8. The computer implemented method ofclaim 1, further comprising: receiving input from the user through theclient device, the input identifies at least one string value in thedata record; and wherein the presenting the sub-set of target keymembers that has been identified with the lowest total cost to the userfurther includes filtering before presenting sub-set of target keymembers that match the at least one string value.
 9. The computerimplemented method of claim 1, further comprising: receiving input thatidentifies at least one sales stage of a sales transaction; and whereinthe presenting the sub-set of target key members that has beenidentified with the lowest total cost to the user further includesfiltering before presenting sub-set of target key members that match theat least one sales stage of a sales transaction.
 10. The computerimplemented method of claim 9, wherein the at least one sales stage ofthe sales transaction is at least one of pre-qualified, qualified,pricing, and proposal.
 11. The computer implemented method of claim 1,wherein each of the set of source key members and the set of target keymembers include a data record with values for the plurality ofadditional sales attributes in the sales decision process furtherincludes one or more of a role in the set of target key members in asales decision process; an impact score representing an influence in theset of target key members in completing a sales transaction; a priorityscore representing a level of lessor or greater importance beingallocated by the set of target key members to the sales transaction; anadvocacy score representing an amount of support by the set of targetkey members for completion of the sale transaction; and an access scorerepresenting an amount of direct access with the set of target keymembers.
 12. The computer implemented method of claim 11, wherein thedata record with values for the plurality of additional sales attributesin the sales decision process further includes; for the set of aplurality of key members, receiving data for populating a data recordwith values for one or more attributes for each key member in the set ofthe plurality of key members including: the role; the impact score; thepriority score; the advocacy score; and the access score.
 13. Thecomputer implemented method of claim 10, further comprising: receivinginput from the user through the client device, the input identifies atleast one sales attribute to filter; and wherein the presenting thesub-set of target key members that has been identified with the lowesttotal cost to the user further includes filtering before presentingsub-set of target key members that match the at least one salesattribute.
 14. A system for managing sales within organizations, thesystem comprising: a computer memory capable of storing machineinstructions; and a hardware processor in communication with thecomputer memory, the hardware processor configured to access thecomputer memory to execute the machine instructions to perform receivinginput from a user through a client device, the input identifies a set ofsource key members; accessing a database of a set of target key members,wherein each of the set of source key members and the set of target keymembers include a data record with values for a plurality of additionalsales attributes in a sales decision process, wherein each of the set ofsource key members and the set of target key members include a datarecord with values for the plurality of additional sales attributes inthe sales decision process further includes one or more of a role in theset of target key members in a sales decision process; an impact scorerepresenting an influence in the set of target key members in completinga sales transaction; a priority score representing a level of lessor orgreater importance being allocated by the set of target key members tothe sales transaction; an advocacy score representing an amount ofsupport by the set of target key members for completion of the saletransaction; and an access score representing an amount of direct accesswith the set of target key members; for each source key member in theset of source key members and each of target key member in the set oftarget key members, performing normalizing a similarity measurement bycalculating a distance between corresponding additional sales attributesin the plurality of additional sales attributes for each of the sourcekey members with each of the target key members to form an array ofsimilarity measurement costs; and applying a combinatorial optimizationalgorithm for solving an assignment problem using the array ofsimilarity measurement costs, such that one corresponding additionalsales attribute of each target key member is assigned to onecorresponding additional sales attribute of each source key member,whereby a total similarity measurement cost of assignment is minimized;identifying a sub-set of target key members in the set of target keymembers with a lowest total similarity measurement cost of assignment;and automatically presenting the sub-set of target key members in one ormore of a deal legitimacy graph with one axis representing the impactscore and another axis representing the priority score, wherein a deallegitimacy position on the deal legitimacy graph is indicated in one ofa plurality of different areas based on the lowest total similaritymeasurement cost of assignment, and a deal position graph with one axisrepresenting the advocacy score and another axis representing the accessscore, wherein a deal position on the deal position graph is indicatedin one of a plurality of different areas based on the lowest totalsimilarity measurement cost of assignment.
 15. The system of claim 14,further comprising for each source key member in the set of source keymembers, normalizing the values for the plurality of additional saleattributes to within a numerical range.
 16. The system of claim 14,wherein the receiving input from the user through the client device, theinput identifies the set of source key members further includes the setof source key members with a first sales attribute; and wherein theaccessing a database of the set of target key members includes accessingthe set of target key members that match the first sales attribute. 17.The system of claim 14, further comprising: receiving input from theuser through the client device, the input identifies at least oneadditional sales attribute to filter; and wherein the presenting thesub-set of target key members that has been identified with the lowesttotal cost to the user further includes filtering before presentingsub-set of target key members that match the at least one additionalsales attribute.
 18. The system of claim 14, further comprising:receiving input from the user through the client device, the inputidentifies at least one additional status of a sales transaction; andwherein the presenting the sub-set of target key members that has beenidentified with the lowest total cost to the user further includesfiltering before presenting sub-set of target key members that match theat least one additional status of the sales transaction.
 19. The systemof claim 18, wherein the at least one additional status of the salestransaction is at least one of open sales transaction, a successfultransaction and an unsuccessful transaction.
 20. The system of claim 14,further comprising: receiving input from the user through the clientdevice, the input identifies at least one competitor; and wherein thepresenting the sub-set of target key members that has been identifiedwith the lowest total cost to the user further includes filtering beforepresenting sub-set of target key members that match the at least onecompetitor.